Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations11688
Missing cells36661
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory439.5 B

Variable types

DateTime2
Numeric14
Categorical3
Text1
Unsupported2

Alerts

city_name has constant value "local"Constant
Autoconsumo (kWh) is highly overall correlated with Horário Económico (kWh)High correlation
Horário Económico (kWh) is highly overall correlated with Autoconsumo (kWh) and 1 other fieldsHigh correlation
Normal (kWh) is highly overall correlated with Horário Económico (kWh)High correlation
clouds_all is highly overall correlated with weather_descriptionHigh correlation
feels_like is highly overall correlated with temp and 2 other fieldsHigh correlation
rain_1h is highly overall correlated with weather_descriptionHigh correlation
temp is highly overall correlated with feels_like and 2 other fieldsHigh correlation
temp_max is highly overall correlated with feels_like and 2 other fieldsHigh correlation
temp_min is highly overall correlated with feels_like and 2 other fieldsHigh correlation
weather_description is highly overall correlated with clouds_all and 1 other fieldsHigh correlation
Data has 672 (5.7%) missing valuesMissing
Hora has 672 (5.7%) missing valuesMissing
Normal (kWh) has 672 (5.7%) missing valuesMissing
Horário Económico (kWh) has 672 (5.7%) missing valuesMissing
Autoconsumo (kWh) has 672 (5.7%) missing valuesMissing
Injeção na rede (kWh) has 672 (5.7%) missing valuesMissing
sea_level has 11688 (100.0%) missing valuesMissing
grnd_level has 11688 (100.0%) missing valuesMissing
rain_1h has 9253 (79.2%) missing valuesMissing
dt is uniformly distributedUniform
DataHora has unique valuesUnique
dt has unique valuesUnique
dt_iso has unique valuesUnique
sea_level is an unsupported type, check if it needs cleaning or further analysisUnsupported
grnd_level is an unsupported type, check if it needs cleaning or further analysisUnsupported
Hora has 459 (3.9%) zerosZeros
Normal (kWh) has 6018 (51.5%) zerosZeros
Horário Económico (kWh) has 6813 (58.3%) zerosZeros
Autoconsumo (kWh) has 5657 (48.4%) zerosZeros
clouds_all has 1882 (16.1%) zerosZeros

Reproduction

Analysis started2025-11-04 23:44:20.900441
Analysis finished2025-11-04 23:44:46.885921
Duration25.99 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

DataHora
Date

Unique 

Distinct11688
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size91.4 KiB
Minimum2021-09-01 00:00:00
Maximum2022-12-31 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-04T23:44:47.017988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:47.156687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Data
Date

Missing 

Distinct459
Distinct (%)4.2%
Missing672
Missing (%)5.7%
Memory size91.4 KiB
Minimum2021-09-29 00:00:00
Maximum2022-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-04T23:44:47.276795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:47.397837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Hora
Real number (ℝ)

Missing  Zeros 

Distinct24
Distinct (%)0.2%
Missing672
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros459
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:47.501810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9225008
Coefficient of variation (CV)0.60195659
Kurtosis-1.2041758
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum126684
Variance47.921017
MonotonicityNot monotonic
2025-11-04T23:44:47.587536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0459
 
3.9%
1459
 
3.9%
2459
 
3.9%
3459
 
3.9%
4459
 
3.9%
5459
 
3.9%
6459
 
3.9%
7459
 
3.9%
8459
 
3.9%
9459
 
3.9%
Other values (14)6426
55.0%
(Missing)672
 
5.7%
ValueCountFrequency (%)
0459
3.9%
1459
3.9%
2459
3.9%
3459
3.9%
4459
3.9%
5459
3.9%
6459
3.9%
7459
3.9%
8459
3.9%
9459
3.9%
ValueCountFrequency (%)
23459
3.9%
22459
3.9%
21459
3.9%
20459
3.9%
19459
3.9%
18459
3.9%
17459
3.9%
16459
3.9%
15459
3.9%
14459
3.9%

Normal (kWh)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1282
Distinct (%)11.6%
Missing672
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.20227805
Minimum0
Maximum3.251
Zeros6018
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:47.717396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.314
95-th percentile0.896
Maximum3.251
Range3.251
Interquartile range (IQR)0.314

Descriptive statistics

Standard deviation0.34947829
Coefficient of variation (CV)1.7277124
Kurtosis8.4381405
Mean0.20227805
Median Absolute Deviation (MAD)0
Skewness2.5386084
Sum2228.295
Variance0.12213507
MonotonicityNot monotonic
2025-11-04T23:44:47.826258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06018
51.5%
0.00123
 
0.2%
0.00819
 
0.2%
0.01817
 
0.1%
0.01617
 
0.1%
0.25217
 
0.1%
0.25317
 
0.1%
0.00616
 
0.1%
0.00316
 
0.1%
0.02615
 
0.1%
Other values (1272)4841
41.4%
(Missing)672
 
5.7%
ValueCountFrequency (%)
06018
51.5%
0.00123
 
0.2%
0.00214
 
0.1%
0.00316
 
0.1%
0.00414
 
0.1%
0.00511
 
0.1%
0.00616
 
0.1%
0.00714
 
0.1%
0.00819
 
0.2%
0.00911
 
0.1%
ValueCountFrequency (%)
3.2511
< 0.1%
3.1871
< 0.1%
2.8621
< 0.1%
2.6561
< 0.1%
2.5851
< 0.1%
2.541
< 0.1%
2.5191
< 0.1%
2.5131
< 0.1%
2.4911
< 0.1%
2.461
< 0.1%

Horário Económico (kWh)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct851
Distinct (%)7.7%
Missing672
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.15971396
Minimum0
Maximum6.978
Zeros6813
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:47.940614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.288
95-th percentile0.584
Maximum6.978
Range6.978
Interquartile range (IQR)0.288

Descriptive statistics

Standard deviation0.27179198
Coefficient of variation (CV)1.7017421
Kurtosis45.200384
Mean0.15971396
Median Absolute Deviation (MAD)0
Skewness3.8686343
Sum1759.409
Variance0.07387088
MonotonicityNot monotonic
2025-11-04T23:44:48.047931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06813
58.3%
0.26334
 
0.3%
0.25631
 
0.3%
0.32129
 
0.2%
0.2627
 
0.2%
0.25926
 
0.2%
0.26226
 
0.2%
0.25125
 
0.2%
0.2724
 
0.2%
0.28123
 
0.2%
Other values (841)3958
33.9%
(Missing)672
 
5.7%
ValueCountFrequency (%)
06813
58.3%
0.0371
 
< 0.1%
0.0381
 
< 0.1%
0.0531
 
< 0.1%
0.0621
 
< 0.1%
0.0681
 
< 0.1%
0.0691
 
< 0.1%
0.072
 
< 0.1%
0.0751
 
< 0.1%
0.0761
 
< 0.1%
ValueCountFrequency (%)
6.9781
< 0.1%
2.6671
< 0.1%
2.6131
< 0.1%
2.3621
< 0.1%
2.1821
< 0.1%
2.1581
< 0.1%
2.1511
< 0.1%
2.0532
< 0.1%
1.9371
< 0.1%
1.9051
< 0.1%

Autoconsumo (kWh)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct752
Distinct (%)6.8%
Missing672
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.11731409
Minimum0
Maximum1.192
Zeros5657
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:48.204942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.227
95-th percentile0.452
Maximum1.192
Range1.192
Interquartile range (IQR)0.227

Descriptive statistics

Standard deviation0.17676216
Coefficient of variation (CV)1.5067429
Kurtosis4.4481169
Mean0.11731409
Median Absolute Deviation (MAD)0
Skewness1.8879191
Sum1292.332
Variance0.031244863
MonotonicityNot monotonic
2025-11-04T23:44:48.331420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05657
48.4%
0.00167
 
0.6%
0.00257
 
0.5%
0.00448
 
0.4%
0.00338
 
0.3%
0.01432
 
0.3%
0.01132
 
0.3%
0.2731
 
0.3%
0.27830
 
0.3%
0.26629
 
0.2%
Other values (742)4995
42.7%
(Missing)672
 
5.7%
ValueCountFrequency (%)
05657
48.4%
0.00167
 
0.6%
0.00257
 
0.5%
0.00338
 
0.3%
0.00448
 
0.4%
0.00523
 
0.2%
0.00629
 
0.2%
0.00721
 
0.2%
0.00827
 
0.2%
0.00923
 
0.2%
ValueCountFrequency (%)
1.1921
< 0.1%
1.1912
< 0.1%
1.1891
< 0.1%
1.1471
< 0.1%
1.1411
< 0.1%
1.1311
< 0.1%
1.1271
< 0.1%
1.1221
< 0.1%
1.1191
< 0.1%
1.1051
< 0.1%

Injeção na rede (kWh)
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing672
Missing (%)5.7%
Memory size611.7 KiB
None
7777 
High
1103 
Medium
1098 
Very High
 
606
Low
 
432

Length

Max length9
Median length4
Mean length4.4351852
Min length3

Characters and Unicode

Total characters48858
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None7777
66.5%
High1103
 
9.4%
Medium1098
 
9.4%
Very High606
 
5.2%
Low432
 
3.7%
(Missing)672
 
5.7%

Length

2025-11-04T23:44:48.423671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-04T23:44:48.503688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none7777
66.9%
high1709
 
14.7%
medium1098
 
9.4%
very606
 
5.2%
low432
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e9481
19.4%
o8209
16.8%
N7777
15.9%
n7777
15.9%
i2807
 
5.7%
H1709
 
3.5%
g1709
 
3.5%
h1709
 
3.5%
M1098
 
2.2%
d1098
 
2.2%
Other values (8)5484
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)48858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9481
19.4%
o8209
16.8%
N7777
15.9%
n7777
15.9%
i2807
 
5.7%
H1709
 
3.5%
g1709
 
3.5%
h1709
 
3.5%
M1098
 
2.2%
d1098
 
2.2%
Other values (8)5484
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9481
19.4%
o8209
16.8%
N7777
15.9%
n7777
15.9%
i2807
 
5.7%
H1709
 
3.5%
g1709
 
3.5%
h1709
 
3.5%
M1098
 
2.2%
d1098
 
2.2%
Other values (8)5484
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9481
19.4%
o8209
16.8%
N7777
15.9%
n7777
15.9%
i2807
 
5.7%
H1709
 
3.5%
g1709
 
3.5%
h1709
 
3.5%
M1098
 
2.2%
d1098
 
2.2%
Other values (8)5484
11.2%

dt
Real number (ℝ)

Uniform  Unique 

Distinct11688
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.651491 × 109
Minimum1.6304544 × 109
Maximum1.6725276 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:48.608096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.6304544 × 109
5-th percentile1.6325581 × 109
Q11.6409727 × 109
median1.651491 × 109
Q31.6620093 × 109
95-th percentile1.6704239 × 109
Maximum1.6725276 × 109
Range42073200
Interquartile range (IQR)21036600

Descriptive statistics

Standard deviation12147046
Coefficient of variation (CV)0.0073551993
Kurtosis-1.2
Mean1.651491 × 109
Median Absolute Deviation (MAD)10519200
Skewness0
Sum1.9302627 × 1013
Variance1.4755071 × 1014
MonotonicityStrictly increasing
2025-11-04T23:44:48.723497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16725276001
 
< 0.1%
16304544001
 
< 0.1%
16304580001
 
< 0.1%
16304616001
 
< 0.1%
16304652001
 
< 0.1%
16304688001
 
< 0.1%
16304724001
 
< 0.1%
16304760001
 
< 0.1%
16723872001
 
< 0.1%
16723908001
 
< 0.1%
Other values (11678)11678
99.9%
ValueCountFrequency (%)
16304544001
< 0.1%
16304580001
< 0.1%
16304616001
< 0.1%
16304652001
< 0.1%
16304688001
< 0.1%
16304724001
< 0.1%
16304760001
< 0.1%
16304796001
< 0.1%
16304832001
< 0.1%
16304868001
< 0.1%
ValueCountFrequency (%)
16725276001
< 0.1%
16725240001
< 0.1%
16725204001
< 0.1%
16725168001
< 0.1%
16725132001
< 0.1%
16725096001
< 0.1%
16725060001
< 0.1%
16725024001
< 0.1%
16724988001
< 0.1%
16724952001
< 0.1%

dt_iso
Text

Unique 

Distinct11688
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size890.4 KiB
2025-11-04T23:44:49.298544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters338952
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11688 ?
Unique (%)100.0%

Sample

1st row2021-09-01 00:00:00 +0000 UTC
2nd row2021-09-01 01:00:00 +0000 UTC
3rd row2021-09-01 02:00:00 +0000 UTC
4th row2021-09-01 03:00:00 +0000 UTC
5th row2021-09-01 04:00:00 +0000 UTC
ValueCountFrequency (%)
000011688
25.0%
utc11688
25.0%
19:00:00487
 
1.0%
23:00:00487
 
1.0%
20:00:00487
 
1.0%
00:00:00487
 
1.0%
01:00:00487
 
1.0%
02:00:00487
 
1.0%
21:00:00487
 
1.0%
04:00:00487
 
1.0%
Other values (503)19480
41.7%
2025-11-04T23:44:50.027861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0124867
36.8%
242673
 
12.6%
35064
 
10.3%
:23376
 
6.9%
-23376
 
6.9%
121067
 
6.2%
+11688
 
3.4%
U11688
 
3.4%
T11688
 
3.4%
C11688
 
3.4%
Other values (7)21777
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)338952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0124867
36.8%
242673
 
12.6%
35064
 
10.3%
:23376
 
6.9%
-23376
 
6.9%
121067
 
6.2%
+11688
 
3.4%
U11688
 
3.4%
T11688
 
3.4%
C11688
 
3.4%
Other values (7)21777
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)338952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0124867
36.8%
242673
 
12.6%
35064
 
10.3%
:23376
 
6.9%
-23376
 
6.9%
121067
 
6.2%
+11688
 
3.4%
U11688
 
3.4%
T11688
 
3.4%
C11688
 
3.4%
Other values (7)21777
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)338952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0124867
36.8%
242673
 
12.6%
35064
 
10.3%
:23376
 
6.9%
-23376
 
6.9%
121067
 
6.2%
+11688
 
3.4%
U11688
 
3.4%
T11688
 
3.4%
C11688
 
3.4%
Other values (7)21777
 
6.4%

city_name
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size616.5 KiB
local
11688 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters58440
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlocal
2nd rowlocal
3rd rowlocal
4th rowlocal
5th rowlocal

Common Values

ValueCountFrequency (%)
local11688
100.0%

Length

2025-11-04T23:44:50.115356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-04T23:44:50.183582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
local11688
100.0%

Most occurring characters

ValueCountFrequency (%)
l23376
40.0%
o11688
20.0%
c11688
20.0%
a11688
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)58440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l23376
40.0%
o11688
20.0%
c11688
20.0%
a11688
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)58440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l23376
40.0%
o11688
20.0%
c11688
20.0%
a11688
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)58440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l23376
40.0%
o11688
20.0%
c11688
20.0%
a11688
20.0%

temp
Real number (ℝ)

High correlation 

Distinct2423
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.406638
Minimum0.32
Maximum40.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:50.263443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile8.2205
Q112.35
median15.76
Q319.54
95-th percentile27.243
Maximum40.85
Range40.53
Interquartile range (IQR)7.19

Descriptive statistics

Standard deviation5.7159767
Coefficient of variation (CV)0.34839416
Kurtosis0.56477119
Mean16.406638
Median Absolute Deviation (MAD)3.62
Skewness0.64852728
Sum191760.78
Variance32.672389
MonotonicityNot monotonic
2025-11-04T23:44:50.383911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5962
 
0.5%
15.755
 
0.5%
16.8251
 
0.4%
12.9246
 
0.4%
16.8145
 
0.4%
17.9241
 
0.4%
15.1739
 
0.3%
11.2639
 
0.3%
12.3737
 
0.3%
15.1537
 
0.3%
Other values (2413)11236
96.1%
ValueCountFrequency (%)
0.321
< 0.1%
0.991
< 0.1%
1.351
< 0.1%
1.81
< 0.1%
2.261
< 0.1%
2.591
< 0.1%
2.61
< 0.1%
2.731
< 0.1%
2.771
< 0.1%
2.951
< 0.1%
ValueCountFrequency (%)
40.851
< 0.1%
39.841
< 0.1%
39.691
< 0.1%
39.411
< 0.1%
39.261
< 0.1%
38.511
< 0.1%
38.471
< 0.1%
38.341
< 0.1%
38.211
< 0.1%
38.061
< 0.1%

feels_like
Real number (ℝ)

High correlation 

Distinct2702
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.076164
Minimum-2.19
Maximum41.33
Zeros1
Zeros (%)< 0.1%
Negative4
Negative (%)< 0.1%
Memory size91.4 KiB
2025-11-04T23:44:50.650997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.19
5-th percentile6.77
Q111.76
median15.675
Q319.56
95-th percentile27.5765
Maximum41.33
Range43.52
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation6.2176046
Coefficient of variation (CV)0.38675921
Kurtosis0.58850497
Mean16.076164
Median Absolute Deviation (MAD)3.905
Skewness0.55373506
Sum187898.21
Variance38.658607
MonotonicityNot monotonic
2025-11-04T23:44:50.776913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.9927
 
0.2%
16.3824
 
0.2%
18.2224
 
0.2%
13.8824
 
0.2%
15.6622
 
0.2%
16.9621
 
0.2%
15.7421
 
0.2%
11.5120
 
0.2%
11.520
 
0.2%
17.5719
 
0.2%
Other values (2692)11466
98.1%
ValueCountFrequency (%)
-2.191
< 0.1%
-0.971
< 0.1%
-0.61
< 0.1%
-0.041
< 0.1%
01
< 0.1%
0.112
< 0.1%
0.221
< 0.1%
0.331
< 0.1%
0.491
< 0.1%
0.611
< 0.1%
ValueCountFrequency (%)
41.331
< 0.1%
40.461
< 0.1%
40.281
< 0.1%
39.871
< 0.1%
39.771
< 0.1%
39.552
< 0.1%
39.141
< 0.1%
39.11
< 0.1%
39.021
< 0.1%
38.981
< 0.1%

temp_min
Real number (ℝ)

High correlation 

Distinct488
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.433353
Minimum-0.64
Maximum36.72
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size91.4 KiB
2025-11-04T23:44:50.898686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.64
5-th percentile6.45
Q110.9
median14.54
Q317.72
95-th percentile22.72
Maximum36.72
Range37.36
Interquartile range (IQR)6.82

Descriptive statistics

Standard deviation4.9605904
Coefficient of variation (CV)0.3436894
Kurtosis0.34749008
Mean14.433353
Median Absolute Deviation (MAD)3.18
Skewness0.22796434
Sum168697.03
Variance24.607457
MonotonicityNot monotonic
2025-11-04T23:44:51.046374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.72471
 
4.0%
16.72400
 
3.4%
15.72335
 
2.9%
18.72309
 
2.6%
19.72296
 
2.5%
14.72243
 
2.1%
12.72239
 
2.0%
13.72225
 
1.9%
20.72223
 
1.9%
21.72185
 
1.6%
Other values (478)8762
75.0%
ValueCountFrequency (%)
-0.641
 
< 0.1%
-0.591
 
< 0.1%
0.121
 
< 0.1%
0.161
 
< 0.1%
0.21
 
< 0.1%
0.461
 
< 0.1%
0.821
 
< 0.1%
1.061
 
< 0.1%
1.383
< 0.1%
1.724
< 0.1%
ValueCountFrequency (%)
36.721
 
< 0.1%
35.722
< 0.1%
35.571
 
< 0.1%
34.541
 
< 0.1%
33.731
 
< 0.1%
33.723
< 0.1%
33.431
 
< 0.1%
33.341
 
< 0.1%
33.031
 
< 0.1%
32.931
 
< 0.1%

temp_max
Real number (ℝ)

High correlation 

Distinct550
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.50299
Minimum1.33
Maximum41.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:51.386583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile9.04
Q112.93
median16.72
Q320.7
95-th percentile29.59
Maximum41.45
Range40.12
Interquartile range (IQR)7.77

Descriptive statistics

Standard deviation6.1123437
Coefficient of variation (CV)0.34921711
Kurtosis0.63762523
Mean17.50299
Median Absolute Deviation (MAD)3.83
Skewness0.80056688
Sum204574.95
Variance37.360746
MonotonicityNot monotonic
2025-11-04T23:44:51.503895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.72313
 
2.7%
13.72249
 
2.1%
14.72223
 
1.9%
10.72178
 
1.5%
16.82174
 
1.5%
16.72163
 
1.4%
12.72162
 
1.4%
11.72160
 
1.4%
11.82158
 
1.4%
12.93156
 
1.3%
Other values (540)9752
83.4%
ValueCountFrequency (%)
1.331
< 0.1%
2.881
< 0.1%
2.891
< 0.1%
3.341
< 0.1%
3.431
< 0.1%
3.81
< 0.1%
3.91
< 0.1%
3.991
< 0.1%
41
< 0.1%
4.041
< 0.1%
ValueCountFrequency (%)
41.452
 
< 0.1%
40.94
< 0.1%
40.343
< 0.1%
39.793
< 0.1%
39.591
 
< 0.1%
39.451
 
< 0.1%
39.234
< 0.1%
39.041
 
< 0.1%
38.685
< 0.1%
38.483
< 0.1%

pressure
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1018.3041
Minimum994
Maximum1034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:51.616628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum994
5-th percentile1009
Q11015
median1018
Q31022
95-th percentile1029
Maximum1034
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.1097274
Coefficient of variation (CV)0.0059999047
Kurtosis0.71890967
Mean1018.3041
Median Absolute Deviation (MAD)4
Skewness-0.31178371
Sum11901938
Variance37.328769
MonotonicityNot monotonic
2025-11-04T23:44:51.738259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1017889
 
7.6%
1018881
 
7.5%
1016875
 
7.5%
1019775
 
6.6%
1015720
 
6.2%
1020708
 
6.1%
1014670
 
5.7%
1021611
 
5.2%
1022594
 
5.1%
1023563
 
4.8%
Other values (31)4402
37.7%
ValueCountFrequency (%)
9943
 
< 0.1%
9955
 
< 0.1%
9969
 
0.1%
99732
0.3%
99831
0.3%
99915
 
0.1%
100025
0.2%
100128
0.2%
100249
0.4%
100349
0.4%
ValueCountFrequency (%)
10341
 
< 0.1%
103326
 
0.2%
103263
 
0.5%
1031136
 
1.2%
1030161
1.4%
1029241
2.1%
1028212
1.8%
1027296
2.5%
1026315
2.7%
1025367
3.1%

sea_level
Unsupported

Missing  Rejected  Unsupported 

Missing11688
Missing (%)100.0%
Memory size91.4 KiB

grnd_level
Unsupported

Missing  Rejected  Unsupported 

Missing11688
Missing (%)100.0%
Memory size91.4 KiB

humidity
Real number (ℝ)

Distinct82
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.619011
Minimum19
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:51.847807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile45
Q166
median81
Q391
95-th percentile94
Maximum100
Range81
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.157421
Coefficient of variation (CV)0.21088005
Kurtosis0.066197588
Mean76.619011
Median Absolute Deviation (MAD)11
Skewness-0.89178711
Sum895523
Variance261.06224
MonotonicityNot monotonic
2025-11-04T23:44:52.433791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93751
 
6.4%
94714
 
6.1%
92513
 
4.4%
91445
 
3.8%
90394
 
3.4%
95388
 
3.3%
89369
 
3.2%
87299
 
2.6%
86285
 
2.4%
88283
 
2.4%
Other values (72)7247
62.0%
ValueCountFrequency (%)
191
 
< 0.1%
204
 
< 0.1%
211
 
< 0.1%
2210
0.1%
234
 
< 0.1%
246
0.1%
255
 
< 0.1%
269
0.1%
2710
0.1%
2813
0.1%
ValueCountFrequency (%)
1002
 
< 0.1%
994
 
< 0.1%
9812
 
0.1%
9716
 
0.1%
96108
 
0.9%
95388
3.3%
94714
6.1%
93751
6.4%
92513
4.4%
91445
3.8%

wind_speed
Real number (ℝ)

Distinct771
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6476882
Minimum0.06
Maximum11.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:52.541405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.76
Q11.61
median2.38
Q33.4
95-th percentile5.5165
Maximum11.1
Range11.04
Interquartile range (IQR)1.79

Descriptive statistics

Standard deviation1.458574
Coefficient of variation (CV)0.55088585
Kurtosis1.2846
Mean2.6476882
Median Absolute Deviation (MAD)0.87
Skewness1.0334736
Sum30946.18
Variance2.127438
MonotonicityNot monotonic
2025-11-04T23:44:52.637337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6553
 
0.5%
1.9451
 
0.4%
1.9349
 
0.4%
1.9849
 
0.4%
2.0648
 
0.4%
1.3848
 
0.4%
1.7848
 
0.4%
1.9548
 
0.4%
1.9646
 
0.4%
2.2545
 
0.4%
Other values (761)11203
95.9%
ValueCountFrequency (%)
0.062
 
< 0.1%
0.081
 
< 0.1%
0.095
< 0.1%
0.11
 
< 0.1%
0.113
< 0.1%
0.132
 
< 0.1%
0.143
< 0.1%
0.152
 
< 0.1%
0.175
< 0.1%
0.183
< 0.1%
ValueCountFrequency (%)
11.11
< 0.1%
10.231
< 0.1%
10.031
< 0.1%
10.021
< 0.1%
9.331
< 0.1%
9.281
< 0.1%
9.271
< 0.1%
9.141
< 0.1%
9.11
< 0.1%
8.961
< 0.1%

rain_1h
Real number (ℝ)

High correlation  Missing 

Distinct371
Distinct (%)15.2%
Missing9253
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean0.88475975
Minimum0.1
Maximum7.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:52.733687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.12
Q10.21
median0.45
Q31.07
95-th percentile3.24
Maximum7.45
Range7.35
Interquartile range (IQR)0.86

Descriptive statistics

Standard deviation1.0734526
Coefficient of variation (CV)1.2132701
Kurtosis7.2020289
Mean0.88475975
Median Absolute Deviation (MAD)0.29
Skewness2.4569753
Sum2154.39
Variance1.1523005
MonotonicityNot monotonic
2025-11-04T23:44:52.846990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1276
 
0.7%
0.1663
 
0.5%
0.1458
 
0.5%
0.1157
 
0.5%
0.1556
 
0.5%
0.1752
 
0.4%
0.1951
 
0.4%
0.1349
 
0.4%
0.2147
 
0.4%
0.244
 
0.4%
Other values (361)1882
 
16.1%
(Missing)9253
79.2%
ValueCountFrequency (%)
0.136
0.3%
0.1157
0.5%
0.1276
0.7%
0.1349
0.4%
0.1458
0.5%
0.1556
0.5%
0.1663
0.5%
0.1752
0.4%
0.1842
0.4%
0.1951
0.4%
ValueCountFrequency (%)
7.451
< 0.1%
71
< 0.1%
6.891
< 0.1%
6.751
< 0.1%
6.741
< 0.1%
6.721
< 0.1%
6.641
< 0.1%
6.611
< 0.1%
6.541
< 0.1%
6.531
< 0.1%

clouds_all
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.258214
Minimum0
Maximum100
Zeros1882
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size91.4 KiB
2025-11-04T23:44:52.958554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median60
Q398
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)91

Descriptive statistics

Standard deviation40.443374
Coefficient of variation (CV)0.74538713
Kurtosis-1.6368486
Mean54.258214
Median Absolute Deviation (MAD)40
Skewness-0.18571671
Sum634170
Variance1635.6665
MonotonicityNot monotonic
2025-11-04T23:44:53.073906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002166
 
18.5%
01882
 
16.1%
99439
 
3.8%
1333
 
2.8%
98319
 
2.7%
2226
 
1.9%
97224
 
1.9%
96201
 
1.7%
95168
 
1.4%
3147
 
1.3%
Other values (91)5583
47.8%
ValueCountFrequency (%)
01882
16.1%
1333
 
2.8%
2226
 
1.9%
3147
 
1.3%
4124
 
1.1%
591
 
0.8%
681
 
0.7%
776
 
0.7%
875
 
0.6%
973
 
0.6%
ValueCountFrequency (%)
1002166
18.5%
99439
 
3.8%
98319
 
2.7%
97224
 
1.9%
96201
 
1.7%
95168
 
1.4%
94113
 
1.0%
93142
 
1.2%
9298
 
0.8%
91101
 
0.9%

weather_description
Categorical

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size706.3 KiB
sky is clear
3160 
overcast clouds
2591 
light rain
1784 
broken clouds
1528 
scattered clouds
1213 
Other values (3)
1412 

Length

Max length20
Median length15
Mean length12.869439
Min length10

Characters and Unicode

Total characters150418
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbroken clouds
2nd rowovercast clouds
3rd rowovercast clouds
4th rowlight rain
5th rowmoderate rain

Common Values

ValueCountFrequency (%)
sky is clear3160
27.0%
overcast clouds2591
22.2%
light rain1784
15.3%
broken clouds1528
13.1%
scattered clouds1213
 
10.4%
few clouds761
 
6.5%
moderate rain587
 
5.0%
heavy intensity rain64
 
0.5%

Length

2025-11-04T23:44:53.168811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-04T23:44:53.252605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
clouds6093
22.9%
sky3160
11.9%
is3160
11.9%
clear3160
11.9%
overcast2591
9.7%
rain2435
 
9.2%
light1784
 
6.7%
broken1528
 
5.7%
scattered1213
 
4.6%
few761
 
2.9%
Other values (3)715
 
2.7%

Most occurring characters

ValueCountFrequency (%)
s16281
10.8%
14912
9.9%
c13057
 
8.7%
e11768
 
7.8%
r11514
 
7.7%
l11037
 
7.3%
o10799
 
7.2%
a10050
 
6.7%
d7893
 
5.2%
t7516
 
5.0%
Other values (12)35591
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)150418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s16281
10.8%
14912
9.9%
c13057
 
8.7%
e11768
 
7.8%
r11514
 
7.7%
l11037
 
7.3%
o10799
 
7.2%
a10050
 
6.7%
d7893
 
5.2%
t7516
 
5.0%
Other values (12)35591
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)150418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s16281
10.8%
14912
9.9%
c13057
 
8.7%
e11768
 
7.8%
r11514
 
7.7%
l11037
 
7.3%
o10799
 
7.2%
a10050
 
6.7%
d7893
 
5.2%
t7516
 
5.0%
Other values (12)35591
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)150418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s16281
10.8%
14912
9.9%
c13057
 
8.7%
e11768
 
7.8%
r11514
 
7.7%
l11037
 
7.3%
o10799
 
7.2%
a10050
 
6.7%
d7893
 
5.2%
t7516
 
5.0%
Other values (12)35591
23.7%

Interactions

2025-11-04T23:44:45.059881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.332235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.601080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.117091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:28.500334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.980306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.582143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.076483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:35.415244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.486331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.748204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.058627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:41.737747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.568051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.156432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.453855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.685105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.214676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:28.599061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.069959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.707650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.165361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:35.602720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.573821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.835794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.146108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.260070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.669077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.255940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.543478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.768926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.307552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:28.693703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.165233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.814800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.264505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:35.967845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.683957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.930643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.243947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.487587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.786719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.345896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.634616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.867544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.405439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:28.790398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.273132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.934041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.356496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.085709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.775522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.026048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.336683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.587740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.883831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.432070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.728774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.959117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.494939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:28.888180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.387267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:32.054808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.439250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.184817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.869301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.120821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.519070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.675908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.064877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.513522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.818385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:24.071912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.587283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.077544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.465195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:32.378933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.516998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.316742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.956580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.211513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.610006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.772425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.156766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.608007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.908880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:24.161402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.676927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.174026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.563877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:32.488031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.614760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.524756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.046009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.309132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.725351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.872058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.246441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.698967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:22.989560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:24.256542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.775824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.263669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.688791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:32.587153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.713786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.640766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.126807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.403571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.805552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:42.957857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.333773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.785960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.078673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:24.345107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.874587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.364927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:30.803712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:32.875412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.808814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.776759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.214889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.495133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.898728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.055107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.454918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.876002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.157194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:25.589233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.969600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.469662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.050900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:33.001938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.898114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:36.875386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.303750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.589661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:40.993599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.136831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.557528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:45.969511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.251283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:25.716925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:27.359295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.590842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.179025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:33.115397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:34.999294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.005819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.398954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.689739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:41.118914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.235072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.657426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:46.058592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.351300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:25.823657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:27.523615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.694060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.265931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:33.224784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:35.088836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.123779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.493889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.787053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:41.214095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.320171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.758490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:46.135009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.435813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:25.922275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:27.617612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.779052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.357371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:33.806113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:35.210678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.298506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.575435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.878725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:41.356542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.399433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.864810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:46.235492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:23.515309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:26.026775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:28.383551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:29.869927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:31.443569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:33.974693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:35.299125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:37.398458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:38.663986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:39.969479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:41.650184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:43.488607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-04T23:44:44.960013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-04T23:44:53.459087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Autoconsumo (kWh)HoraHorário Económico (kWh)Injeção na rede (kWh)Normal (kWh)clouds_alldtfeels_likehumiditypressurerain_1htemptemp_maxtemp_minweather_descriptionwind_speed
Autoconsumo (kWh)1.0000.146-0.6150.4710.092-0.100-0.0100.388-0.478-0.012-0.1300.4110.4040.4210.0590.164
Hora0.1461.000-0.3950.3860.452-0.0570.0020.186-0.223-0.002-0.0210.1960.2050.2150.0310.079
Horário Económico (kWh)-0.615-0.3951.0000.055-0.6500.0470.076-0.3210.3270.016-0.001-0.338-0.334-0.3570.013-0.148
Injeção na rede (kWh)0.4710.3860.0551.0000.1370.1170.1300.2500.2870.0670.0600.2580.2650.2540.1140.087
Normal (kWh)0.0920.452-0.6500.1371.0000.101-0.131-0.0110.0160.0190.060-0.007-0.0080.0220.0300.090
clouds_all-0.100-0.0570.0470.1170.1011.0000.107-0.0930.486-0.2020.321-0.116-0.138-0.0560.6870.139
dt-0.0100.0020.0760.130-0.1310.1071.0000.1830.138-0.2850.1400.1690.1820.1560.1550.068
feels_like0.3880.186-0.3210.250-0.011-0.0930.1831.000-0.396-0.3150.0070.9980.9830.9620.128-0.049
humidity-0.478-0.2230.3270.2870.0160.4860.138-0.3961.000-0.0770.202-0.445-0.440-0.3810.202-0.036
pressure-0.012-0.0020.0160.0670.019-0.202-0.285-0.315-0.0771.000-0.283-0.303-0.309-0.3230.167-0.154
rain_1h-0.130-0.021-0.0010.0600.0600.3210.1400.0070.202-0.2831.000-0.002-0.0120.0030.9060.360
temp0.4110.196-0.3380.258-0.007-0.1160.1690.998-0.445-0.303-0.0021.0000.9850.9630.125-0.041
temp_max0.4040.205-0.3340.265-0.008-0.1380.1820.983-0.440-0.309-0.0120.9851.0000.9540.123-0.039
temp_min0.4210.215-0.3570.2540.022-0.0560.1560.962-0.381-0.3230.0030.9630.9541.0000.1030.017
weather_description0.0590.0310.0130.1140.0300.6870.1550.1280.2020.1670.9060.1250.1230.1031.0000.210
wind_speed0.1640.079-0.1480.0870.0900.1390.068-0.049-0.036-0.1540.360-0.041-0.0390.0170.2101.000

Missing values

2025-11-04T23:44:46.393641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-04T23:44:46.573650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-04T23:44:46.773958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DataHoraDataHoraNormal (kWh)Horário Económico (kWh)Autoconsumo (kWh)Injeção na rede (kWh)dtdt_isocity_nametempfeels_liketemp_mintemp_maxpressuresea_levelgrnd_levelhumiditywind_speedrain_1hclouds_allweather_description
02021-09-01 00:00:00NaNNaNNaNNaNNaNNaN16304544002021-09-01 00:00:00 +0000 UTClocal18.7418.8415.7220.341015NaNNaN831.18NaN78broken clouds
12021-09-01 01:00:00NaNNaNNaNNaNNaNNaN16304580002021-09-01 01:00:00 +0000 UTClocal18.7318.8315.7220.341014NaNNaN831.46NaN92overcast clouds
22021-09-01 02:00:00NaNNaNNaNNaNNaNNaN16304616002021-09-01 02:00:00 +0000 UTClocal17.8417.9816.7220.341014NaNNaN881.05NaN91overcast clouds
32021-09-01 03:00:00NaNNaNNaNNaNNaNNaN16304652002021-09-01 03:00:00 +0000 UTClocal18.2718.4016.6820.341014NaNNaN860.460.1494light rain
42021-09-01 04:00:00NaNNaNNaNNaNNaNNaN16304688002021-09-01 04:00:00 +0000 UTClocal17.8117.9716.1220.341013NaNNaN890.931.2695moderate rain
52021-09-01 05:00:00NaNNaNNaNNaNNaNNaN16304724002021-09-01 05:00:00 +0000 UTClocal17.8918.0616.1217.931014NaNNaN891.100.9694light rain
62021-09-01 06:00:00NaNNaNNaNNaNNaNNaN16304760002021-09-01 06:00:00 +0000 UTClocal18.2718.4816.1218.481014NaNNaN891.210.1193light rain
72021-09-01 07:00:00NaNNaNNaNNaNNaNNaN16304796002021-09-01 07:00:00 +0000 UTClocal19.4419.6916.6819.591015NaNNaN861.274.5595heavy intensity rain
82021-09-01 08:00:00NaNNaNNaNNaNNaNNaN16304832002021-09-01 08:00:00 +0000 UTClocal18.9219.1916.6819.041015NaNNaN892.333.3899moderate rain
92021-09-01 09:00:00NaNNaNNaNNaNNaNNaN16304868002021-09-01 09:00:00 +0000 UTClocal18.9819.2817.2319.041015NaNNaN902.230.52100light rain
DataHoraDataHoraNormal (kWh)Horário Económico (kWh)Autoconsumo (kWh)Injeção na rede (kWh)dtdt_isocity_nametempfeels_liketemp_mintemp_maxpressuresea_levelgrnd_levelhumiditywind_speedrain_1hclouds_allweather_description
116782022-12-31 14:00:002022-12-3114.01.0600.0000.166None16724952002022-12-31 14:00:00 +0000 UTClocal17.8417.0416.4517.931017NaNNaN526.73NaN41scattered clouds
116792022-12-31 15:00:002022-12-3115.01.3400.0000.039None16724988002022-12-31 15:00:00 +0000 UTClocal17.3516.8116.6817.371017NaNNaN648.35NaN77broken clouds
116802022-12-31 16:00:002022-12-3116.01.2440.0000.001None16725024002022-12-31 16:00:00 +0000 UTClocal16.7916.3815.9016.821017NaNNaN717.680.4392light rain
116812022-12-31 17:00:002022-12-3117.02.1010.0000.000None16725060002022-12-31 17:00:00 +0000 UTClocal16.2715.9416.1216.721017NaNNaN767.311.4795moderate rain
116822022-12-31 18:00:002022-12-3118.02.3340.0000.000None16725096002022-12-31 18:00:00 +0000 UTClocal15.7615.5314.7916.721017NaNNaN826.742.9299moderate rain
116832022-12-31 19:00:002022-12-3119.01.6930.0000.000None16725132002022-12-31 19:00:00 +0000 UTClocal15.7015.5715.5715.721018NaNNaN865.563.58100moderate rain
116842022-12-31 20:00:002022-12-3120.01.3270.0000.000None16725168002022-12-31 20:00:00 +0000 UTClocal15.5515.4312.7215.701018NaNNaN874.474.20100heavy intensity rain
116852022-12-31 21:00:002022-12-3121.00.7570.0000.000None16725204002022-12-31 21:00:00 +0000 UTClocal13.4513.2812.2313.991019NaNNaN933.294.23100heavy intensity rain
116862022-12-31 22:00:002022-12-3122.00.0000.6750.000None16725240002022-12-31 22:00:00 +0000 UTClocal12.9312.7312.2313.431019NaNNaN941.493.90100moderate rain
116872022-12-31 23:00:002022-12-3123.00.0000.4030.000None16725276002022-12-31 23:00:00 +0000 UTClocal12.9312.7612.2313.431019NaNNaN951.934.07100heavy intensity rain